Premium
Optimization of K‐edge imaging with spectral CT
Author(s) -
He Peng,
Wei Biao,
Cong Wenxiang,
Wang Ge
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4754587
Subject(s) - enhanced data rates for gsm evolution , bin , energy (signal processing) , image quality , edge detection , attenuation , noise (video) , k edge , spectral imaging , signal to noise ratio (imaging) , edge enhancement , medical imaging , optics , image processing , mathematics , computer science , artificial intelligence , physics , image (mathematics) , algorithm , statistics , absorption spectroscopy
Purpose: Spectral/multienergy CT has the potential to distinguish different materials by K‐edge characteristics. K‐edge imaging involves the two energy bins on both sides of a K‐edge. The authors propose a K‐edge imaging optimization model to determine these two energy bins. Methods: K‐edge image contrast with spectral CT depends on the specifications of the two energy bins on both sides of a K‐edge in the attenuation profile of a relatively high atomic number material. The wider the energy bin width is, the lower the noise level is, and the poorer the reconstructed image contrast is. Here the authors introduce the signal difference to noise ratio (SDNR) criterion to optimize the energy bin widths on both sides of the K‐edge for the maximum SDNR. Results: The authors study K‐edge imaging with spectral CT, analyze the effect of K‐edge energy bins on the resultant image quality, and establish guidelines for the optimization of energy thresholds. In simulation, the authors demonstrate that our K‐edge imaging optimization approach maximizes SDNR in reconstructed images. Conclusions: This proposed approach can be readily generalized to deal with more general settings and determine the best energy bins for K‐edge imaging.